The data augmentations include random flipping, half body transformation, random scale (\(0.5\) , \(1.5\) ), random rotation (\(-40^{\circ }\) , \(40^{\circ }\) ), grid dropout and color jitterring (h=\(0.2\) , s=\(0.4\) , c=\(0.4\) , b=\(0.4\) ). The input image size is \(256\times 256\)  by default. We use the AdamW [1]} optimizer with the base learning rate \(5e\) -4 and the weight decay \(5e\) -2. The learning rate is dropped to \(5e\) -5 at the \(120th\)  epoch. We totally train the models for 150 epochs. We use a layer decay of \(0.9\) /\(0.85\)  for Swin-B/L and the DropPath [2]} of \(0.3\) /\(0.5\)  for Swin-B/L.
The batch size is 512.
